Information fusion for cocaine dependence recognition using fMRI

摘要

Cocaine dependence devastates millions of human lives. Despite of a variety of treatments, there is a very high rate of individual relapse to drug use. In the last decade, functional magnetic resonance imaging (fMRI) proved to be a powerful tool to diagnose and understand different pathologies. This work provides advances in the identification of cocaine dependence and in the relapse prediction based on fMRI classification. We improve the traditional methodology of the literature called multi-voxel pattern analysis (MVPA), which is used for feature extraction and classification. In addition, we propose new features that use specific functional connectivity measures. An extensive evaluation was conducted comparing our methodology with MVPA, as well as, several learning methods with distinct feature sets. We could identify the neural patterns that lead to improve classification accuracies and evaluate the advantages of employing an information fusion approach through an ensemble of classifiers. Experimental results show an improvement of final accuracy over the state-of-the-art methods.